Iris Identification Using

1

Iris Identification Using
Wavelet Packets

Emine Krichen, Mohamed Anouar Mellakh, Sonia
Garcia Salicetti, Bernadette Dorizzi


{emine.krichen,anouar
-
mellakh;sonia.salicetti;bernadette.dorizzi}@int
-
evry.fr


Institut National des Télécommunications

9 Rue Charles Fourier , 91011 Evry France

2

Outline

•
Classical

approach

versus

our

approach

(Packets

Method)

•
Experimentations

on

2

databases

•
Introduction

of

color

information

•
Conclusion

and

perspectives




3

Introduction

•
Study of

iris recognition on normal
light illumination

–
Use
of

usual devices

–
Fusion

between

iris

and

other

biometric

modalities

(face,

eye

shape
…
)


4

Comparison infra
-
red / normal light


Normal light Near Infra red

•

Lack of texture information

•

Presence of a great number of reflections

5

Iris Segmentation

Hough Transform (Iris circle)

Circular Edge detector

6

Wavelet method

•
2D wavelet basis : Gabor



•
Spatial parameters in
polar coordinates (
ρ
,
θ
).

•
4

resolution

levels

•
2048 coefficients for
coding the iris.











d
φ
d
ρ
ρ
φ
ρ,
I
e
e
e
2
2
0
2
2
0
0
β
φ
θ
α
ρ
r
φ
θ
i
ω











J. Daugman, “How iris recognition works”, Proceedings of the International
Conference on Image Processing, 22
-
25 September 2002


7

Our approach : Packet method

•
Process the whole
image
at each level
of resolution

•
Starting with higher
mother wavelet
window

•
1664 coefficients for
coding iris



8

Databases

•
IrisINT

:

Iris

images

recorded

under

normal

light

illumination
.

70

persons

700

images
.




•
CASIA

:

Iris

images

taken

under

infra

red

illumination
.

110

persons,

770

images
.

Recorded

at

NLPR

China
.


9

Roc curves (IrisINT)

•
Poor results for the wavelet method

•
The wavelet Packet method is more
robust using visible light images


10

Comparative results on CASIA
and IrisINT

Databases

IrisINT


CASIA

Type of errors

FAR

FRR

FAR

FRR

Classical wavelet method

2%

12.04%

0.35%

2.08%

Packets method

0%

0.57%

0.2%

1.38%

•

With

infra

red

illumination,

the

two

methods

have

quite

the

same

performance
.

WP

is

more

robust

to

the

presence

of

eyelids

or

eyelashes
.


C.P. Strouthopoulos, Adaptive
color reduction

11

Use of color information

ACR method

Original color image

(71.000 different colors)

Color image (256 colors)

We

perform

iris

recognition

using

the

same

algorithm

as

the

one

developed

for

grey

level

image

12

Use of color information :

ROC curve on IrisINT

Use of color information allows a better
discrimination between the persons.

13

Conclusion and perspectives

•
The

packets

method

allows

better

performance

on

normal

light

illumination

images
.

•
Color

information

can

be

used

to

improve

results

on

simple

grey

level

images
.

•
Results

need

to

be

confirmed

using

larger

bimodal

database

(in

order

to

decrease

the

variance)
.


14

Adaptive color reduction (ACR)

Self organized neural network

Reduction adapted to initial distribution of colors


N
.

Papamarkos,

A
.
E
.

Atsalakis,

and

C
.
P
.

Strouthopoulos,

Adaptive

colour

reduction,

IEEE

Transactions

on

Systems,

Man,

and

Cybernetics
,

Vol
.

32
,

N
°
1
,

,

February

2002
.



RGB +
neighborhood
information

One

Neuron

per color